# TLCD: A Deep Transfer Learning Framework for Cross-Disciplinary Cognitive Diagnosis

arXiv: https://arxiv.org/abs/2510.23062 · HTML: https://mlsanity.com/paper/2510.23062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 78 references that are in the map.
- First posted on arXiv 27 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [Abs-CAM: A Gradient Optimization Interpretable Approach for Explanation of Convolutional Neural Networks](https://mlsanity.com/paper/2207.03648/) (2022)
- [Knowledge Relation Rank Enhanced Heterogeneous Learning Interaction Modeling for Neural Graph Forgetting Knowledge Tracing](https://mlsanity.com/paper/2304.03945/) (2023)
- [Knowledge Graph Enhanced Intelligent Tutoring System Based on Exercise Representativeness and Informativeness](https://mlsanity.com/paper/2307.15076/) (2023)
- [Spatio-Temporal Representation Learning Enhanced Source Cell-phone Recognition from Speech Recordings](https://mlsanity.com/paper/2208.12753/) (2022)
- [Leveraging Label Semantics and Meta-Label Refinement for Multi-Label Question Classification](https://mlsanity.com/paper/2411.01841/) (2024)
- [UKTF: Unified Knowledge Tracing Framework for Subjective and Objective Assessments](https://mlsanity.com/paper/2411.05325/) (2024)

## Abstract (arXiv)

Driven by the dual principles of smart education and artificial intelligence technology, the online education model has rapidly emerged as an important component of the education industry. Cognitive diagnostic technology can utilize students' learning data and feedback information in educational evaluation to accurately assess their ability level at the knowledge level. However, while massive amounts of information provide abundant data resources, they also bring about complexity in feature extraction and scarcity of disciplinary data. In cross-disciplinary fields, traditional cognitive diagnostic methods still face many challenges. Given the differences in knowledge systems, cognitive structures, and data characteristics between different disciplines, this paper conducts in-depth research on neural network cognitive diagnosis and knowledge association neural network cognitive diagnosis, and proposes an innovative cross-disciplinary cognitive diagnosis method (TLCD). This method combines deep learning techniques and transfer learning strategies to enhance the performance of the model in the target discipline by utilizing the common features of the main discipline. The experimental results show that the cross-disciplinary cognitive diagnosis model based on deep learning performs better than the basic model in cross-disciplinary cognitive diagnosis tasks, and can more accurately evaluate students' learning situation.